20 Global Education Review 5(1)
Global Education Review is a publication of The School of Education at Mercy College, New York. This is an Open Access article distributed under the terms of the Creative
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properly cited. Citation: Zhou, Kai (2018). What cognitive neuroscience tells us about creativity education: A literature review. Global Education
Review, 5 (1), 20-34.
What Cognitive Neuroscience Tells Us About
Creativity Education: A Literature Review
Kai Zhou
State University of New York at Albany
Abstract
Recently, an interest in creativity education has increased globally. Cognitive neuroscience research of
creativity has provided possible implications for education, yet few literary reviews that bridge the brain
and education studies have been published. This article first introduces the definitions and behavioral
measures of creativity from cognitive neuroscientists’ perspectives and provides a brief overview on the
brain regions and neural studies on creativity-related cognitive processes. Second, the article examines
neuroscience studies on the relationship between creativity and intelligence and discusses the nature side
of creativity. Third, a comprehensive review of cognitive neuroscience studies on activities that may
trigger new creativity thinking is provided, followed by a discussion on the nurture side of creativity--
more specifically--how these findings inform creativity education. Supportive evidence from research in
cognitive psychology and education are also presented. Then the article discusses the policy implications
of the findings from the literature review as they pertain to creativity skills development in formal
education and training.
Keywords
creativity, nature and nurture, neuroscience, skills, educational policy
Introduction
A growing body of literature has emerged on the
influence of creativity on individual life and
social economic outcomes. Research has shown
that creativity is rewarded with wage premium
(Gabe, Colby, & Bell, 2007), positive affect at
work (Tavares, 2016), as well as health and
well- being (Greaves, 2006). Policy-makers also
have noted the critical role of creative workforce
plays in transforming industrial economies to
technology-driven knowledge economies. New
technology-based companies and innovative
start-up businesses which depend heavily on
creative and skilled workers have a unique share
in the economy, producing new jobs and
contributing to economic growth of a country
(Reynolds, 2010). The number of creative
entrepreneurs who started small and medium-
sized enterprises has grown rapidly, accounting
for more than 10% of the labor market
workforce in many countries (Fairlie &
Holleran, 2012). Florida, Mellander, & Stolarick
(2008) found that the creative class, who
represent about 30 percent of the U.S.
____________________________
Corresponding Author:
Kai Zhou, Department of Educational Policy and Leadership,
State University of New York at Albany, 1400 Washington
Avenue, Albany, NY 12222.
Email: [email protected]
Neuroscience and creativity education 21
workforce, has significant positive association
with regional labor productivity.
Given the importance of creativity in
determining individual and social outcomes, the
interests in integrating creativity and innovation
development into the education system have
been increasing (Sawyer, 2006). However,
empirical studies have shown that creativity has
little or no association with academic
achievement (e.g., Ai, 1999; Balgiu & Adîr,
2014). It is therefore possible to say that either
the creativity is not accurately captured by
behavioral assessments that have been used by
psychologists or creativity has not been properly
measured by standardized achievement tests in
schools. The uncertainty may hinder
researchers, educators and policy makers from
drawing out convincing educational policy
implications from empirical work. Only
recently, technology opened a door to more
direct and comprehensive research on
creativity. Cognitive neuroscience has emerged
as an important approach that allows
researchers to understand what happens inside
the brain when performing creative tasks. The
development of neuroscience may reshape the
discussion on creativity education.
This paper is organized into four sections
to provide an up-to-date review of cognitive and
neuroscience research on creativity and discuss
the policy implications for creativity education.
The first section contains a brief review on the
definitions and cognitive measures of creativity,
as well as the brain regions and structures in
creative cognition processes. The second section
updates research findings on the relationship
between creativity and intelligence and
discusses the heritability of creativity. The third
section focuses on the nurture side of creativity
and presents neuroscientific evidence on
activities that may trigger new creativity
thinking. The final section discusses the use of
neuroscientific research for policy implications
as they pertain to creativity skills development
in formal education and training.
Cognitive Neuroscience Research
in Creativity
Creativity is “the ability to produce work that is
both novel (i.e., original, unexpected) and
appropriate (i.e., useful, adaptive concerning
task constraints)” (Sternberg & Lubart, 1999, p.
3). Boden (2004) classifies creativity into three
types: combinational, exploratory or
transformational based on the psychological
processing features involved in innovative
thinking. Combinational creativity involves
combining familiar things and ideas in a
surprising way. An example can be creating a
new flavored cake by putting in unexpected
ingredients. Exploratory creativity is realized by
generating new ideas or artifacts within an
existing conceptual space based on the
established culturally-accepted rules and
conventional style of thinking (Boden, 2013, p.
6). Improving the equipment efficiency by using
better materials and creating new music are
examples. Transformational creativity entails
the creation of shocking things and ideas that
were “impossible” before, which are beyond the
existing conceptual space or specific stylistic
limits. For example, the pioneering idea that the
earth orbits the sun instead of the sun going
around the earth was the result of disruptive
creativity in ancient times. Exploratory and
transformational creativity are both defined
within a certain sociocultural space; ideas or
artifacts are produced before they are
recognized as “creative”. Combinational,
exploratory and transformational creativity can
either appear in one innovative idea or artifact
at the same time or separately.
Cognitive neuroscience depends heavily
on analyses of associative pathways and
relevance in human brain system during
creative behaviors. Thus, cognitive
22 Global Education Review 5(1)
psychologists and neuroscientists can carefully
examine only combinational creativity so far.
Because of the sociocultural features,
exploratory and transformational creativity can
only be explained through post hoc testing after
the work is valued as exploratory or
transformational creative (Boden, 2013).
Exploratory or transformational creativity is far
from being stimulable and their occurrence is
rare. Though cognitive neuroscience studies can
compare creative and non-creative individuals
on their brain structure or the way of cognitive
processing, the studies do not draw any causal
conclusion on what factors affect exploratory
and transformational creativity. Since the value
of exploratory and transformational creativity
has been more valued by the society, the lack of
relevant neuroscience evidence on these two
types of creativity makes it challenging to
translate the brain findings into educational
practice, not to mention initiating large-scale
policy changes. Yet, the neuroscience approach
has made some great contributions to the
understanding of combinational creativity
processes (Sawyer, 2012), providing a good start
for cross- disciplinary discussions.
Neuroscientists use cognitive behavioral
assessment of combinational creativity
frequently in their studies to identify brain
changes while people are engaged in cognitive
tasks. The goal is to explain the combinational
creativity thinking in a neurobiological way. On
the cognitive behavioral level, creativity can be
measured by a number of indicators: divergent
thinking (McCrae, 1987; Runco & Acar, 2012),
represented by originality (Beghetto, 2010);
ideational fluency (Snyder, Mitchell,
Bossomaier, & Pallier, 2004); cognitive
flexibility (Ghacibeh, Shenker, Shenal, Uthman,
& Heilman, 2006) and elaboration (Takeuchi et
al., 2011); convergent thinking in making
unique associations and solving insight
problems (Arden, Chavez, Grazioplene, & Jung,
2010; Dietrich & Kanso, 2010); and vivid
imagination (Karwowski, Jankowska, &
Szwajkowski, 2017; Roberts et al., 2017).
Various standardized behavioral assessments
on creativity have been developed since the
1950s, among which the most widely used tests
include the Torrance Test of Creative Thinking
(TTCT) (Torrance, 1972), Alternative Uses
Tasks (Guilford, 1967), Remote Associates Test
(Mednick, 1962, 1971), and Creative
Functioning Test (Smith & Carlsson, 1987). In a
cognitive neuroscience experiment, researchers
adopt a cognitive assessment and ask
participants to perform a series of simple tasks.
While a person is engaged in a task, brain
activities are captured to show what’s
happening in the mind. Neuroscientists employ
electroencephalography (EEG), functional
magnetic resonance imaging (fMRI), and
positron emission tomography (PET) to study
brain regions and neurons and explore the
neural mechanisms underlying combinational
creative thinking (Gabora & Ranjan, 2013).
These technologies sometimes are applied
together to provide more thorough
understanding of the brain activities associated
with cognitive functioning of creativity.
The theory of left-brain or right-brain
dominance has been widely accepted by
educators and policy makers. The right brain
has been traditionally regarded as the prime
organ that controls creativity and innovation. A
number of right-brain training programs that
involve art, music and drama, in particular,
have been carried out to help young children
“stimulate” right brain areas and “strengthen”
their creativity-thinking functions.
Nevertheless, no evidence from cognitive
neuroscience has been found that a particular
brain area for creativity exists (Sawyer, 2012).
Creativity involves the whole brain. The right
and the left hemispheres play a critical but
disparate role at different stages of the creative
process, and collaborate in different creative
tasks, the same as they do for other cognitive
Neuroscience and creativity education 23
function (Sawyer, 2012). When a person
engages in creative thinking, the left
hemisphere of the brain, which is dominant for
analytic and verbal processes, works together
with the right hemisphere, which is associated
with natural perceptual, whole-pattern, spatial
processes (Kaufman, Kornilov, Bristol, Tan, &
Grigorenko, 2010). Though some studies have
shown that the right and left hemispheres have
closer communication and more dynamic
collaboration during creativity activities (Lezak,
2012, p. 69; Whitman, Holcomb, & Zanes,
2010), creativity results from ordinary mental
processes. Neural circuits coBrainmbine
information in both creative and non-creative
way (Dietrich, 2004).
The prefrontal cortex which is known for
its “executive” functions in integrating complex
information has been shown to be the central
structure to enable higher-order processing,
including but not limited to innovative thinking
(Dietrich, 2004). The prefrontal cortex
navigates attention, stores working memory and
supports temporal integration (Funahashi &
Andreau, 2013; Fuster, 2001). Already highly
processed information from different sensory
modalities is further screened and aggregated
for higher cognitive functions, such as flexibility
of cognitive control (Rougier, Noelle, Braver,
Cohen, & O’Reilly, 2005), reflective processing
(van der Meer, Costafreda, Aleman, & David,
2010), and reasoning (Krawczyk, 2012) which
are associated with creative thinking. The
prefrontal cortex intentionally chooses what
information an individual attends to and
preserves the selected contents for a period that
allows creativity to happen. Meanwhile,
Dietrich (2004) suggested that the prefrontal
cortex also acts like a search engine that can
retrieve relevant elements from long-term
memory stored in the temporal, occipital and
parietal lobes (TOP) area to form new
recombinations.
Brain study scientists have found that
individuals who are highly creative are
biologically different from those with low
creativity. Carlsson, Wendt, & Risberg (2000)
revealed that individuals who performed very
well on the Guilford’s Alternate Uses creativity
test tended to have higher regional blood flow in
both the left and right frontal lobes than those
who got very low scores, which implied a
positive association between activation of
frontal cortex and creativity. This positive
correlation was also confirmed by Gibson,
Folley, & Park (2009) using a near-infrared
spectroscopy (NIRS) method. The researchers
compared creativity and frontal cortical activity
between a group of trained creative musicians
and a demographically matched control group.
The results indicated that creative individuals
experienced greater bilateral frontal activity
than noncreative individuals while performing
divergent thinking. Jung et al. (2009) found
that the cortical thickness in a region within the
lingual gyrus and left lateral orbitofrontal area
was negatively linked to creativity, whereas
higher cortical thickness in the right posterior
cingulate and right angular gyrus was
associated with higher scores on a creativity
test.
Scientists also found positive associations
between regional gray matter volume (rGMV)
and several creativity indicators, such as
ideational fluency, combinational fluency,
originality, and cognitive flexibility in the
precuneus (Fink et al., 2014; Jauk, Neubauer,
Dunst, Fink, & Benedek, 2015; Kühn et al.,
2014; Takeuchi et al., 2010). These
neuroscientific findings provided evidence that
creativity thinking ability is associated with
brain structures. However, a snapshot of the
differences between creative and non-creative
individuals is not sufficient to get an insight of
whether highly creative individuals were born
with these different brain structures or
developed them later in life through education
and training. Longitudinal investigations are
24 Global Education Review 5(1)
needed in the future to provide implications for
creativity education and training.
Nature or Nurture?
There has long been an argument on whether
creativity is a heritable trait or determined by
the environment, and to what extent education
can foster creativity ability. Some researchers
claimed that creativity is a subcategory of
intelligence (e.g., Guilford & Christensen, 1973)
which has been found to have genetic origin
(e.g., Posthuma et al., 2002). However, findings
on the relationship between creativity and
intelligence have been mixed. Some behavioral
studies have shown that creative individuals are
more likely to perform well on general
intelligence tests (e.g., Barron & Harrington,
1981, p. 445). Others have found that
intelligence is not a good predictor of creativity
(e.g., Hocevar, 1980; Subotnik, Karp, &
Morgan, 1989). Individuals who score high in
the intelligence quotient (IQ) are not noticeably
creative (Terman & Oden, 1959). Today, most
researchers agree that creativity and
intelligence are associated up to a certain
point—around an average IQ of 120, while
correlations in the higher IQ is negligible (Cho,
Nijenhuis, Vianen, Kim, & Lee, 2010; Sligh,
Conners, & Roskos-Ewoldsen, 2005). The
current neuroscience literature on intelligence
and creativity has further provided brain
imaging evidence that intelligence lays genetic
foundation for the occurrence of creative
processes but is not sufficient to ensure the
complex brain to exhibit creativity (Haier &
Jung, 2008). Basically, most neuroscientists
supported the claims that genetically reflected
intelligence is largely responsible for the neural
efficiency in the general cognitive functions
(Fink, Benedek, Grabner, Staudt, & Neubauer,
2007; Grabner, Fink, Stipacek, Neuper, &
Neubauer, 2004; Neubauer, Grabner,
Freudenthaler, Beckmann, & Guthke, 2004),
whereas environmental factors are mostly
responsible for creative quality and output
(Haier & Jung, 2008; Sawyer, 2012).
Since 1870s, twin studies have been used
as one of the best approaches to evaluate the
heritability of creative abilities. Most twin
studies based on behavioral cognitive approach
failed to reveal convincing evidence of a genetic
basis for creativity (Sawyer, 2012, p. 181). For
example, Reznikoff, Domino, Bridges, &
Honeyman (1973) administered ten creativity
tests to 117 pairs of adolescent twins. The
subjects were divided into four groups—28
pairs of identical males, 35 pairs of identical
females, 19 pairs of fraternal males and 35 pairs
of fraternal females. The researchers didn’t find
proof of a genetic component in creative
abilities. However, emerging evidence from
brain imaging has told a different story. Schmitt
et al. (2014) conducted a longitudinal study
collecting 1,748 anatomic MRI scans from 792
healthy twins and siblings. Their findings
indicated that both genetic and environmental
factors had significant contributions to the
variance in cortical thickness change in
prefrontal cortex, which has been shown to be
related to creative activities (Jung et al., 2009).
Some genetic analyses of creativity
released recently also supported the existence of
the nature side of creativity. Reuter, Roth,
Holve, & Hennig (2006) proposed the first
candidate gene for creativity through a test on
92 healthy Caucasian individuals while
controlling for intelligence. They found that D2
Dopamine Receptor (DRD2) gene and
Tryptophane Hydroxylase (TPH1) gene were
associated with total creativity, accounting for
9% of the variance. Runco et al., (2011)
replicated and extended the analyses to include
a test on five candidate genes. They found that
ideational fluency scores were significantly
related to Dopamine Transporter (DAT),
Catechol-O-Methyltransferase (COMT),
Dopamine Receptor D4 (DRD4), and
Tryptophane Hydroxylase (TPH1). Volf,
Neuroscience and creativity education 25
Kulikov, Bortsov, & Popova (2009) identified
the 5-HTTLPR polymorphism of the
neurotransmitter serotonin transporter gene (5-
HTT) to be associated significantly with
divergent thinking. Other researchers have also
found the genetic basis of creativity-related
cognitive factors (e.g., Kéri, 2009; Smalley, Loo,
Yang, & Cantor, 2005). However, though
studies have shown a clear genetic basis for
some creativity cognitive components, the
extent to which the genes contribute to the
manifestation of individuals’ creativity is not
within sight (Runco et al., 2011).
The cognitive neuroscience research is
still in its infancy. How intelligence and
creativity are distinctly or commonly expressed
in the brain structures and regions,
organization, and networks has not yet been
thoroughly researched. The genetic
contributions to creativity need further
exploration through cross-disciplinary efforts,
combining neuroscience with psychology,
genetics, molecular biology and others. It’s now
generally accepted that all creative activities
have a genetic basis. But creativity is a complex
phenomenon that involves a large number of
behavioral characteristics (Treffinger, 2009)
and different cognitive processes in various
brain regions and structures (Sawyer, 2012),
each of which have interactions with the
environment, the inheritability of creativity is
limited to some extent (Barbot, Tan, &
Grigorenko, 2013). Thus, we have good reasons
to argue that it’s possible to foster creativity
from a variety of aspects through quality
educational practices. Findings from brain
studies in the near future may allow educators
to target those underlying components of
creativity and focus effort to achieve creativity
education in school (Vartanian, 2013).
Neuroscience and Creativity
Education
A substantial amount of evidence has
accumulated to show the possibility of
enhancing creativity via targeted cognitive
education and trainings, most of which came
from the analyses of behavioral data (Scott,
Leritz, & Mumford, 2004; Tsai, 2013).
Diversifying experiences (Ritter et al., 2012),
episodic memory activation (Madore, Addis, &
Schacter, 2015; Madore, Jing, & Schacter,
2016), improvisation activities (Sawyer, 2006;
Sowden, Clements, Redlich, & Lewis, 2015) and
puzzle based open-ended tasks (Ramaraj &
Nagammal, 2017) are examples of creativity
training that have been shown to be effective
based on behavioral creativity assessments. But
behavioral observations in creativity are limited
in capturing the exact cognitive processing
changes related to educational practices.
Cognitive neuroscience research that examines
both behavioral changes and how these changes
correspond to the structural and functional
changes in the brain is a powerful approach to
provide insights about the intervention
effectiveness in education.
Structural and functional plasticity in the
brain in correlation with behavioral changes
from education and training has been well
documented (Vartanian, 2013). For example,
Hyde et al. (2009) found significant changes in
brain structures that are related to musically
relevant motor and auditory skills after 15
months of music training. Rueda, Rothbart,
McCandliss, Saccomanno, & Posner (2005)
investigated the efficiency of attentional control
training in neural network which involves the
anterior cingulate in addition to lateral
prefrontal areas. The researchers compared
individuals with 5 days training and individuals
with different types of no training and recorded
the event-related potentials from the scalp
during attention network test performance.
26 Global Education Review 5(1)
They found that the attentional mechanisms
and their neural activities in the brain were
malleable through intervention. The training
group had significant improvement in executive
attention and intelligence. Takeuchi et al.
(2013) found that a 4-week working memory
training program induced changes in functional
connectivity and cerebral blood flow involving
the default mode network and the external
attention system during rest. Klingberg (2010)
reviewed literature on working memory training
effects and suggested that adaptive and
extended training in working memory, which is
fundamental to creative thinking, can lead to
changes in brain activity in frontal and parietal
cortex and basal ganglia, as well as changes in
the density of dopamine receptor. All these
neuroscientific findings have implied that
education and training focusing on music,
attentional control, and working memory can
significantly change an individual’s brain
structurally and functionally. It can improve
innovation-related cognitive skills, including
motor and auditory skills, executive attention,
intelligence and working memory.
There has been a growing body of
research that examines the efficiency of
creativity- related education and training by
combining evidence from behavioral effects and
the neural system underlying the transfer
effects. For more than five decades, creativity
education has closely bonded with the arts
(Sawyer, 2012, p. 391). But criticisms have been
raised that the creativity features in the arts
education may not be transferable to other
domains. Results from cognitive behavioral
analyses on the association between music or
visual arts education and cross-domain
creativity were contradictory (Hetland &
Winner, 2004). Recently, neuroscientists began
to explore the cognitive benefits of arts
education, providing evidence from the neural
data of biological brain. For instance, Lopata,
Nowicki, & Joanisse (2017) compared skilled
musicians who had training in musical
improvisation with individuals who had no
formal improvisation training in their frontal
upper alpha-band activity recorded by EEG
during creative and non-creative tasks and
objective ratings on creativity performances.
They found that spontaneous processing of
creative ideas can be effectively fostered
through formal improvisation training.
Similarly, Fink, Graif, & Neubauer (2009)
investigated EEG activity in professional
dancers compared to a group of novices with no
comprehensive training in the field during
performance of different creative dancing tasks.
They found that professional dancers showed
more right-hemispheric alpha synchronization
then the novices did during improvisation dance
tasks but not during imagining dancing tasks.
The researchers also measured brain activity of
the two groups during performance of the
Alternative Uses test. They found that
professional dancers showed stronger alpha
synchronization in posterior parietal brain
regions than novice dancers when performing
the creativity test. These neuroscientific
research findings have suggested that formal
arts education may enhance creativity abilities,
improvisation and generating alternative ideas
in particular.
Brain imaging evidence on non-arts
creativity education and trainings have also
been documented recently. For example,
researchers designed a 5-week creativity
capacity building program (CCBP) as a targeted
creativity intervention class offered to students
at the Stanford Design Institute. The training
program allowed participants to experience
applied creativity, spontaneity, uncertainty and
“failing fast,” the reduction of bias and rapid
prototyping through a cycle of five phases—
observe, brainstorm, synthesize, prototype and
implement, and participants were asked to
repeat the cycle when necessary (Hawthorne et
al., 2014; Kienitz et al., 2014). Researchers
Neuroscience and creativity education 27
administered CCBP in parallel with a 5-week
language capacity building training program
(LCBP) as a control intervention, and measured
creativity before and after the CCBP/LCBP
training on the Torrance Test of Creative
Thinking-Figural (TTCT-F) to examine the
effectiveness of CCBP. Kienitz et al. (2014)
found that CCBP resulted in significantly
greater increase in the performance on two
facets of creativity assessed in TTCT-F—
resistance to premature closure and
elaboration. Hawthorne et al. (2014) illustrated
these findings further by investigating the
neural correlates of creativity in both CCBP and
findLCBP groups as reported. Another example
is that of Sun et al. (2016) who implemented 20
sessions of cognitive stimulation to train
individuals on creative thinking. Longitudinal
analyses in this study showed that at the
behavioral level, individuals performed better in
both the originality and the fluency of divergent
thinking after training. At the neural level,
functional changes were found in the dorsal
anterior cingulate cortex, dorsal lateral
prefrontal cortex and posterior brain regions
after the training. Increase in the gray matter
volume in the dorsal anterior cingulate cortex
was also observed after divergent thinking
training. Neuroscience research has provided
evidence that some short-term, well-arranged,
non-arts creativity education and trainings are
effective in improving individuals’ creativity
thinking. Educators and policy makers may
consider introducing some practices into the
classroom that were proven by neuroscience
and behavioral research to be effective in
creativity education.
However, studies that investigated the
role of a specific creativity training or education
play in both behavioral changes and neural
manifestations of creative thinking are still very
limited so far. As mentioned in the first section
of this article, creativity depends greatly on
integrated fundamental cognitive abilities
developed from daily non-creative activities
(Sawyer, 2012, p. 158). Creative thinking is
linked to the activation of brain regions and
biological changes that are associated with
different fundamental cognitive processing
activities, such as attention and working
memory. Given the multi-facets phenomenon
and the complex combination of ordinary
cognitive features, many neuroscience
implications for educational interventions on
creativity improvement came from research
focusing on optimizing attention and working
memory.
Behavioral research has revealed that
creativity is associated with a wider breadth of
attention that allows individuals to collect more
information at the same time (Kasof, 1997;
Memmert, 2007). If individuals can attend to
more things concurrently, they are more likely
to have more diverse and a greater number of
elements to combine, connect and construct,
increasing the possibility of creative thought
(Martindale, 1999). Creativity has also been
found to be related to efficient selective
attention that inhibits irrelevant information
and facilitates relevant information to boost the
production of original and useful ideas
(Kharkhurin, 2011). Thus, cognitive training
that helps expand attention and optimize
selective attention may lead to better creative
thinking (Takeuchi et al., 2013). Consistent with
this notion, Liu et al. (2012) provided neural
evidence on the potential cognitive benefits of
attention training. The researchers investigated
the activity patterns in the brain during the
creative process—spontaneous lyrical
improvisation for individuals who had free-style
arts practice and the brain image results
suggested that a state of defocused attention
may enable the novel generation, characterized
by disassociated activity in medial and
dorsolateral prefrontal cortices. Additionally, in
a fMRI study, Fink et al. (2010) found that
cognitive stimulation via idea sharing with
28 Global Education Review 5(1)
other people which could have resulted in a
modulation of bottom-up attention can enhance
originality. This creativity behavioral
performance was found to be linked to
increased activation in right- hemispheric
temporo-parietal, medial frontal, and posterior
cingulate cortices, bilaterally. Although there
are few studies that analyze the neural and
behavioral data together to examine the
effectiveness of attention-creativity training, it’s
promising that creativity can be developed
through well-designed training focusing on
attention.
Researchers also found that working
memory capability can predict a wide range of
creative activities based on behavioral
observations (e.g., De Dreu, Nijstad, Baas,
Wolsink, & Roskes, 2012; Lee & Therriault,
2013). Recently, Vartanian et al. (2013)
extended previous research by combining brain
imaging and cognitive behavioral approach to
examine the relationships between working
memory training and creativity. They
administered the Alternate Uses Task (AUT)
creative test in the fMRI scanner in both
experiment groups who received working
memory training and the control group who
engaged in a choice reaction time task that is
not related to working memory. They found that
the experiment group showed significantly
lower activation in ventrolateral prefrontal and
dorsolateral prefrontal cortices, which are
known to be associated with divergent thinking,
than the control group, even though
performance variance on the AUT was not
found between the two groups. The results
suggested that a short regimen of working
memory training can moderate prefrontal
cortex neural function in divergent thinking.
In sum, cognitive neuroscience literature
on the direct effects of training on creativity is
limited. However, based on the results from
fMRI and EEG studies of creativity-related
training, researchers believe that it’s very likely
to increase neural efficiency in creative thinking
through cognitive behavioral interventions
(Vartanian et al., 2013). Brain studies have
shown great possibilities in developing
creativity through education.
Implications for Educational
Policy
In this literature review, the main findings from
the cognitive and neuroscience studies on
creativity are the following: (a) Creativity is a
complex construct defined within a specific
sociocultural context and the neural techniques
today can only explain a small part of creativity;
(b) There is no particular brain area for
creativity. Instead, creativity depends on
integrated activation of brain regions and
biological changes that are related to a variety of
basic cognitive functions; (c) Creativity is
heritable to some extent while it can be fostered
through education and training; and (d)
Creativity can be developed through arts
education and systematic creativity training
programs, as well as targeted training on
fundamental cognitive abilities such as
attention and working memory. Cognitive
neuroscience has made significant progress in
enriching our understanding of creativity and
how to foster creative cognition. It has great
potential for playing a role in education reform
by providing brain-based implications for policy
and practice changes that aim at creating a
creative workforce in a knowledge economy.
There have been a growing number of
countries that prioritize creativity learning in
the education system. Many countries and
regions, including but not limited to Austria,
Belgium, Bhutan, Bulgaria, Czech Republic,
Denmark, Estonia, Germany, Greece, Finland,
France, Ireland, Hungary, Italy, Latvia,
Lithuania, Luxembourg, Malta, Portugal,
Republic of Korea, Romania, Singapore,
Slovakia, Slovenia, Spain, Sweden, UK, Canada,
and the U.S, have a similar agenda for the arts
Neuroscience and creativity education 29
and creativity education but they differ in
approach (Heilmann & Werner, 2010; Sharp &
Le Metais, 2000; Zhou, 2017). For instance,
Northern Ireland and Singapore include
creativity in all curriculum areas, whereas the
Republic of Korea emphasizes different aspects
of creativity distinctively in the objectives of
primary, lower secondary education and upper
secondary education (Sharp & Le Metais,
2000). In its recent national education
framework, Malta highlights discovery and
creativity in its early education learning
objectives.
Cognitive neuroscience studies have
confirmed that educational training can
improve an individual’s creative thinking.
However, not all educational practices in
creativity development have been demonstrated
to be effective. So far, many countries depend
greatly on arts education to develop individuals’
creative abilities. Some countries have
integrated arts in other subject areas to reach a
broader transferability of creativity skills
(Heilmann & Werner, 2010). As mentioned
earlier, a few brain studies have suggested that
arts education may enhance creativity in general
but current evidence is not sufficient to defend
that arts education can generate cross-domain
creative cognition skill. Instead of teaching
creativity through arts education, neuroscience
research had implied that general creativity
education can be extended to focus on basic
cognitive skills development in working
memory and attention, which have been proven
to be closely linked to creative abilities.
Despite the fact that neuroscience has
continuously provided important scientific
implications for educators and policy makers,
putting brain-based theory or findings into
universal classroom practice is still not near and
challenging to reach. Many practitioners and
policy makers fail to interpret and use scientific
facts correctly. A bridge between neuroscience
and education is lacking (Fischer, Goswami, &
Geake, 2010) and neuroscientific messages are
often distorted (Howard-Jones, 2014). To
increase the impact from neuroscience on
creativity education policy, it is necessary to
communicate brain findings in an
understandable way at all levels of the
stakeholders and educate the general public
(Akil et al., 2016). Meanwhile, given that
creativity is defined within a particular
sociocultural context, we will need to collect
more neuroscience evidence from different
sociocultural backgrounds. Currently, solid
brain-based international studies that integrate
both biological measurements and sociocultural
information are very limited. There is a need for
policy makers to identify a systematic
assessment plan to evaluate curricular
effectiveness of culturally different creativity
programs. Then, educators can collaborate with
educational researchers and neuroscientists to
create a database that supports evidence-based
creativity education.
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About the Author
Kai Zhou is a senior administrator and research analyst at
Orange County Public Schools, Florida. She is also a doctoral
candidate from the Department of Educational Policy and
Leadership, University at Albany, State University of New
York. Her articles have appeared in European Journal of
Education, The International Journal of Higher Education
Research and the UNESCO Global Monitoring Report. Her
research interests are currently focused on the relationship
between soft skills, education and labor market outcomes.